Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Numeric12
Categorical2

Alerts

Product ID has a high cardinality: 10000 distinct values High cardinality
Air temperature [K] is highly correlated with Process temperature [K]High correlation
Process temperature [K] is highly correlated with Air temperature [K]High correlation
Rotational speed [rpm] is highly correlated with Torque [Nm]High correlation
Torque [Nm] is highly correlated with Rotational speed [rpm]High correlation
Machine failure is highly correlated with HDF and 2 other fieldsHigh correlation
HDF is highly correlated with Machine failureHigh correlation
PWF is highly correlated with Machine failureHigh correlation
OSF is highly correlated with Machine failureHigh correlation
Air temperature [K] is highly correlated with Process temperature [K]High correlation
Process temperature [K] is highly correlated with Air temperature [K]High correlation
Rotational speed [rpm] is highly correlated with Torque [Nm]High correlation
Torque [Nm] is highly correlated with Rotational speed [rpm]High correlation
Machine failure is highly correlated with HDF and 2 other fieldsHigh correlation
HDF is highly correlated with Machine failureHigh correlation
PWF is highly correlated with Machine failureHigh correlation
OSF is highly correlated with Machine failureHigh correlation
Air temperature [K] is highly correlated with Process temperature [K]High correlation
Process temperature [K] is highly correlated with Air temperature [K]High correlation
Rotational speed [rpm] is highly correlated with Torque [Nm]High correlation
Torque [Nm] is highly correlated with Rotational speed [rpm]High correlation
Machine failure is highly correlated with HDF and 2 other fieldsHigh correlation
HDF is highly correlated with Machine failureHigh correlation
PWF is highly correlated with Machine failureHigh correlation
OSF is highly correlated with Machine failureHigh correlation
UDI is highly correlated with Air temperature [K] and 1 other fieldsHigh correlation
Air temperature [K] is highly correlated with UDI and 1 other fieldsHigh correlation
Process temperature [K] is highly correlated with UDI and 1 other fieldsHigh correlation
Rotational speed [rpm] is highly correlated with Torque [Nm] and 1 other fieldsHigh correlation
Torque [Nm] is highly correlated with Rotational speed [rpm] and 2 other fieldsHigh correlation
Machine failure is highly correlated with Torque [Nm] and 4 other fieldsHigh correlation
TWF is highly correlated with Machine failureHigh correlation
HDF is highly correlated with Machine failureHigh correlation
PWF is highly correlated with Rotational speed [rpm] and 2 other fieldsHigh correlation
OSF is highly correlated with Machine failureHigh correlation
RNF is highly skewed (γ1 = 22.87957015) Skewed
UDI is uniformly distributed Uniform
Product ID is uniformly distributed Uniform
UDI has unique values Unique
Product ID has unique values Unique
Tool wear [min] has 120 (1.2%) zeros Zeros
Machine failure has 9661 (96.6%) zeros Zeros
TWF has 9954 (99.5%) zeros Zeros
HDF has 9885 (98.9%) zeros Zeros
PWF has 9905 (99.1%) zeros Zeros
OSF has 9902 (99.0%) zeros Zeros
RNF has 9981 (99.8%) zeros Zeros

Reproduction

Analysis started2022-04-04 06:54:54.183884
Analysis finished2022-04-04 06:55:57.087338
Duration1 minute and 2.9 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

UDI
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:25:57.753553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.89568
Coefficient of variation (CV)0.5773214038
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.667
MonotonicityStrictly increasing
2022-04-04T12:25:58.045976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
66711
 
< 0.1%
66641
 
< 0.1%
66651
 
< 0.1%
66661
 
< 0.1%
66671
 
< 0.1%
66681
 
< 0.1%
66691
 
< 0.1%
66701
 
< 0.1%
66721
 
< 0.1%
Other values (9990)9990
99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
100001
< 0.1%
99991
< 0.1%
99981
< 0.1%
99971
< 0.1%
99961
< 0.1%
99951
< 0.1%
99941
< 0.1%
99931
< 0.1%
99921
< 0.1%
99911
< 0.1%

Product ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
M14860
 
1
L53850
 
1
L53843
 
1
L53844
 
1
L53845
 
1
Other values (9995)
9995 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowM14860
2nd rowL47181
3rd rowL47182
4th rowL47183
5th rowL47184

Common Values

ValueCountFrequency (%)
M148601
 
< 0.1%
L538501
 
< 0.1%
L538431
 
< 0.1%
L538441
 
< 0.1%
L538451
 
< 0.1%
L538461
 
< 0.1%
H360811
 
< 0.1%
L538481
 
< 0.1%
L538491
 
< 0.1%
L538511
 
< 0.1%
Other values (9990)9990
99.9%

Length

2022-04-04T12:25:58.289968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m148601
 
< 0.1%
m148681
 
< 0.1%
m148771
 
< 0.1%
l471821
 
< 0.1%
l471831
 
< 0.1%
l471841
 
< 0.1%
m148651
 
< 0.1%
l471861
 
< 0.1%
l471871
 
< 0.1%
m148691
 
< 0.1%
Other values (9990)9990
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
L
6000 
M
2997 
H
1003 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowL
3rd rowL
4th rowL
5th rowL

Common Values

ValueCountFrequency (%)
L6000
60.0%
M2997
30.0%
H1003
 
10.0%

Length

2022-04-04T12:25:58.471728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-04T12:25:58.647038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
l6000
60.0%
m2997
30.0%
h1003
 
10.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Air temperature [K]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct93
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.00493
Minimum295.3
Maximum304.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:25:58.817035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum295.3
5-th percentile297.1
Q1298.3
median300.1
Q3301.5
95-th percentile303.5
Maximum304.5
Range9.2
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.000258683
Coefficient of variation (CV)0.006667419375
Kurtosis-0.8359616726
Mean300.00493
Median Absolute Deviation (MAD)1.6
Skewness0.1142739205
Sum3000049.3
Variance4.001034799
MonotonicityNot monotonic
2022-04-04T12:25:59.072034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300.7279
 
2.8%
298.9231
 
2.3%
297.4230
 
2.3%
300.5229
 
2.3%
298.8227
 
2.3%
300.6216
 
2.2%
298.2208
 
2.1%
302.3203
 
2.0%
297.5198
 
2.0%
300.4198
 
2.0%
Other values (83)7781
77.8%
ValueCountFrequency (%)
295.33
 
< 0.1%
295.43
 
< 0.1%
295.518
0.2%
295.638
0.4%
295.718
0.2%
295.819
0.2%
295.910
 
0.1%
2966
 
0.1%
296.112
 
0.1%
296.226
0.3%
ValueCountFrequency (%)
304.51
 
< 0.1%
304.47
 
0.1%
304.315
 
0.1%
304.240
0.4%
304.146
0.5%
30445
0.4%
303.958
0.6%
303.875
0.8%
303.796
1.0%
303.678
0.8%

Process temperature [K]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.00556
Minimum305.7
Maximum313.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:25:59.344036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum305.7
5-th percentile307.7
Q1308.8
median310.1
Q3311.1
95-th percentile312.5
Maximum313.8
Range8.1
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.483734219
Coefficient of variation (CV)0.004786153575
Kurtosis-0.499734365
Mean310.00556
Median Absolute Deviation (MAD)1.1
Skewness0.01502726777
Sum3100055.6
Variance2.201467233
MonotonicityNot monotonic
2022-04-04T12:25:59.610036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310.6317
 
3.2%
310.8273
 
2.7%
310.7266
 
2.7%
308.6265
 
2.6%
310.5263
 
2.6%
310.1260
 
2.6%
308.5257
 
2.6%
310.4254
 
2.5%
311246
 
2.5%
310.9245
 
2.5%
Other values (72)7354
73.5%
ValueCountFrequency (%)
305.72
 
< 0.1%
305.83
 
< 0.1%
305.96
 
0.1%
30614
0.1%
306.117
0.2%
306.226
0.3%
306.313
0.1%
306.48
 
0.1%
306.59
 
0.1%
306.613
0.1%
ValueCountFrequency (%)
313.82
 
< 0.1%
313.74
 
< 0.1%
313.616
 
0.2%
313.522
 
0.2%
313.424
0.2%
313.329
0.3%
313.250
0.5%
313.150
0.5%
31355
0.5%
312.943
0.4%

Rotational speed [rpm]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct941
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1538.7761
Minimum1168
Maximum2886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:25:59.890034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1168
5-th percentile1332
Q11423
median1503
Q31612
95-th percentile1868.05
Maximum2886
Range1718
Interquartile range (IQR)189

Descriptive statistics

Standard deviation179.2840959
Coefficient of variation (CV)0.11651084
Kurtosis7.392944899
Mean1538.7761
Median Absolute Deviation (MAD)91
Skewness1.993171005
Sum15387761
Variance32142.78705
MonotonicityNot monotonic
2022-04-04T12:26:00.258398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145248
 
0.5%
143543
 
0.4%
144742
 
0.4%
142940
 
0.4%
146940
 
0.4%
147940
 
0.4%
145039
 
0.4%
150739
 
0.4%
141839
 
0.4%
144638
 
0.4%
Other values (931)9592
95.9%
ValueCountFrequency (%)
11681
 
< 0.1%
11811
 
< 0.1%
11831
 
< 0.1%
11921
 
< 0.1%
12001
 
< 0.1%
12023
< 0.1%
12071
 
< 0.1%
12081
 
< 0.1%
12122
< 0.1%
12171
 
< 0.1%
ValueCountFrequency (%)
28861
< 0.1%
28741
< 0.1%
28611
< 0.1%
28331
< 0.1%
28251
< 0.1%
27601
< 0.1%
27371
< 0.1%
27211
< 0.1%
27101
< 0.1%
27091
< 0.1%

Torque [Nm]
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct577
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.98691
Minimum3.8
Maximum76.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:00.522620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile23.5
Q133.2
median40.1
Q346.8
95-th percentile56.1
Maximum76.6
Range72.8
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation9.968933725
Coefficient of variation (CV)0.2493049282
Kurtosis-0.01324061402
Mean39.98691
Median Absolute Deviation (MAD)6.8
Skewness-0.00951659584
Sum399869.1
Variance99.37963962
MonotonicityNot monotonic
2022-04-04T12:26:00.783616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.252
 
0.5%
38.550
 
0.5%
42.450
 
0.5%
35.850
 
0.5%
37.749
 
0.5%
39.948
 
0.5%
40.648
 
0.5%
38.248
 
0.5%
4047
 
0.5%
36.647
 
0.5%
Other values (567)9511
95.1%
ValueCountFrequency (%)
3.81
< 0.1%
4.21
< 0.1%
4.61
< 0.1%
5.61
< 0.1%
5.81
< 0.1%
81
< 0.1%
8.81
< 0.1%
9.31
< 0.1%
9.72
< 0.1%
9.81
< 0.1%
ValueCountFrequency (%)
76.61
< 0.1%
76.21
< 0.1%
75.41
< 0.1%
74.51
< 0.1%
73.61
< 0.1%
72.81
< 0.1%
721
< 0.1%
71.81
< 0.1%
71.61
< 0.1%
71.31
< 0.1%

Tool wear [min]
Real number (ℝ≥0)

ZEROS

Distinct246
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.951
Minimum0
Maximum253
Zeros120
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:01.094619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.95
Q153
median108
Q3162
95-th percentile206.05
Maximum253
Range253
Interquartile range (IQR)109

Descriptive statistics

Standard deviation63.65414664
Coefficient of variation (CV)0.5896577766
Kurtosis-1.166737132
Mean107.951
Median Absolute Deviation (MAD)55
Skewness0.02729223905
Sum1079510
Variance4051.850384
MonotonicityNot monotonic
2022-04-04T12:26:01.360619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0120
 
1.2%
269
 
0.7%
563
 
0.6%
758
 
0.6%
5958
 
0.6%
16657
 
0.6%
11957
 
0.6%
955
 
0.5%
14654
 
0.5%
9654
 
0.5%
Other values (236)9355
93.5%
ValueCountFrequency (%)
0120
1.2%
269
0.7%
334
 
0.3%
434
 
0.3%
563
0.6%
631
 
0.3%
758
0.6%
836
 
0.4%
955
0.5%
1045
 
0.4%
ValueCountFrequency (%)
2531
 
< 0.1%
2511
 
< 0.1%
2463
< 0.1%
2443
< 0.1%
2422
< 0.1%
2411
 
< 0.1%
2403
< 0.1%
2391
 
< 0.1%
2382
< 0.1%
2371
 
< 0.1%

Machine failure
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0339
Minimum0
Maximum1
Zeros9661
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:01.569413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1809808427
Coefficient of variation (CV)5.338667925
Kurtosis24.54648607
Mean0.0339
Median Absolute Deviation (MAD)0
Skewness5.151851785
Sum339
Variance0.03275406541
MonotonicityNot monotonic
2022-04-04T12:26:01.730412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09661
96.6%
1339
 
3.4%
ValueCountFrequency (%)
09661
96.6%
1339
 
3.4%
ValueCountFrequency (%)
1339
 
3.4%
09661
96.6%

TWF
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046
Minimum0
Maximum1
Zeros9954
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:01.899047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06767051005
Coefficient of variation (CV)14.71098044
Kurtosis212.5027621
Mean0.0046
Median Absolute Deviation (MAD)0
Skewness14.6444618
Sum46
Variance0.00457929793
MonotonicityNot monotonic
2022-04-04T12:26:02.073042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09954
99.5%
146
 
0.5%
ValueCountFrequency (%)
09954
99.5%
146
 
0.5%
ValueCountFrequency (%)
146
 
0.5%
09954
99.5%

HDF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0115
Minimum0
Maximum1
Zeros9885
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:02.238046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1066249825
Coefficient of variation (CV)9.271737607
Kurtosis82.0097546
Mean0.0115
Median Absolute Deviation (MAD)0
Skewness9.164788742
Sum115
Variance0.01136888689
MonotonicityNot monotonic
2022-04-04T12:26:02.398046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09885
98.9%
1115
 
1.1%
ValueCountFrequency (%)
09885
98.9%
1115
 
1.1%
ValueCountFrequency (%)
1115
 
1.1%
09885
98.9%

PWF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0095
Minimum0
Maximum1
Zeros9905
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:02.570268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09700871646
Coefficient of variation (CV)10.21144384
Kurtosis100.3235037
Mean0.0095
Median Absolute Deviation (MAD)0
Skewness10.11451625
Sum95
Variance0.009410691069
MonotonicityNot monotonic
2022-04-04T12:26:02.732271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09905
99.1%
195
 
0.9%
ValueCountFrequency (%)
09905
99.1%
195
 
0.9%
ValueCountFrequency (%)
195
 
0.9%
09905
99.1%

OSF
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0098
Minimum0
Maximum1
Zeros9902
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:02.902272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.09851360562
Coefficient of variation (CV)10.05240874
Kurtosis97.09985639
Mean0.0098
Median Absolute Deviation (MAD)0
Skewness9.953915634
Sum98
Variance0.009704930493
MonotonicityNot monotonic
2022-04-04T12:26:03.066265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09902
99.0%
198
 
1.0%
ValueCountFrequency (%)
09902
99.0%
198
 
1.0%
ValueCountFrequency (%)
198
 
1.0%
09902
99.0%

RNF
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019
Minimum0
Maximum1
Zeros9981
Zeros (%)99.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2022-04-04T12:26:03.230407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04354973775
Coefficient of variation (CV)22.9209146
Kurtosis521.579046
Mean0.0019
Median Absolute Deviation (MAD)0
Skewness22.87957015
Sum19
Variance0.001896579658
MonotonicityNot monotonic
2022-04-04T12:26:03.390304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
09981
99.8%
119
 
0.2%
ValueCountFrequency (%)
09981
99.8%
119
 
0.2%
ValueCountFrequency (%)
119
 
0.2%
09981
99.8%

Interactions

2022-04-04T12:25:40.467724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:07.856725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:11.687789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:14.793793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:17.999174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:20.871803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:23.783806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:26.666496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:29.392575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:32.265522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:34.990529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:37.833820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:40.682725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:09.083072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:11.926790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:15.050792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:18.235179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:21.225808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:24.006809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:26.872493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:29.612551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:32.474529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:35.209528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:38.038821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:40.902725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:09.309081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:12.165795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:15.296796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:18.468173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:21.439807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:24.230857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:27.081498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:29.826550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:32.679528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:35.467520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:38.250820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:41.158730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:09.563071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:12.488790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:15.713796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:18.719174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:21.683807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:24.483857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:27.320493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:30.070546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:32.923523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:35.715528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:38.498820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:41.390723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:09.886076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:12.749790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:15.950795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:18.967173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:21.914807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:24.721125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:27.557497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:30.302551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:33.170523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:35.940523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:38.727816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:41.663730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:10.111790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:12.977794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:16.199795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:19.220173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:22.159803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:24.964150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:27.804498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:30.560550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:33.417526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:36.175529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:38.954232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:41.883730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:10.349793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:13.204795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:16.444795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:19.458547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:22.394803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:25.192151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:28.026498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:30.781547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:33.649523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:36.391529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:39.174966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:42.095730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:10.561795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:13.629789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:16.703795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:19.689547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:22.614807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:25.416498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:28.253498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:31.005502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:33.874524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:36.608528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:39.390969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:42.437725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:10.773795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:13.873791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:16.946795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:19.920541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:22.838802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:25.642495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:28.485580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:31.226503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:34.099529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:36.829528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:39.606730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:42.644724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:10.985795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:14.095788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:17.206795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:20.153543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:23.071804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:25.865493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:28.708579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:31.458497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:34.328523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:37.178528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:39.819730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:42.856730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:11.229796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:14.351795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:17.498794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:20.399541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:23.313807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:26.094495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:28.929582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:31.813496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:34.552528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:37.401524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:40.036730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:43.070725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:11.456795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:14.575791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:17.743174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:20.633177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:23.552805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:26.457498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:29.152582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:32.039527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:34.773528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:37.614821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-04T12:25:40.252730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-04T12:26:03.701515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-04T12:26:04.150575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-04T12:26:04.625574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-04T12:26:04.967569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-04T12:25:43.427728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-04T12:25:54.181845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

UDIProduct IDTypeAir temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Machine failureTWFHDFPWFOSFRNF
01M14860M298.1308.6155142.800.000000
12L47181L298.2308.7140846.330.000000
23L47182L298.1308.5149849.450.000000
34L47183L298.2308.6143339.570.000000
45L47184L298.2308.7140840.090.000000
56M14865M298.1308.6142541.9110.000000
67L47186L298.1308.6155842.4140.000000
78L47187L298.1308.6152740.2160.000000
89M14868M298.3308.7166728.6180.000000
910M14869M298.5309.0174128.0210.000000

Last rows

UDIProduct IDTypeAir temperature [K]Process temperature [K]Rotational speed [rpm]Torque [Nm]Tool wear [min]Machine failureTWFHDFPWFOSFRNF
99909991L57170L298.8308.5152736.230.000000
99919992M24851M298.9308.4182726.150.000000
99929993L57172L298.8308.4148439.280.000000
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